Registration Check-in Desk closing at 6 pm. Badge pickup.

In recent two years, the COVID-19 pandemic continues to disrupt the world, and has changed most aspects of human life. Healthcare AI has a mission to help humans to tackle the issues that are caused by COVID-19, e.g., COVID-19 vaccine related prediction, COVID-19 medical imaging diagnosis. With the development of the epidemic, the virus keeps mutating, and meanwhile the related research is also evolving. As a result, more and more understanding, observation, policy are involved into daily life. All of these factors bring new challenges and opportunities to scientific research, including Healthcare AI. The goal of this workshop is to bring together perspectives from multiple disciplines (e.g., Healthcare AI, Machine Learning, Medical Image ML, Bioinformatics, Genomics, Epidemiology, Public Health, Health Policy, Computer Vision, Deep Learning, Cognitive Science) to highlight major open questions and to identify collaboration opportunities to address outstanding challenges in the domain of COVID-19 related Healthcare AI.
Website: https://healthcare-ai-covid19.github.io/

Machine learning advances are used in self-driving cars, speech recognition systems, and translation software. However, the COVID-19 pandemic has highlighted the urgency of translating such advances to the domain of biomedicine. Such a pivot requires new machine learning methods to build long-term vaccines and therapeutic strategies, predict immune avoidance, and better repurpose small molecules as drugs.The ICML Workshop on Computational Biology (WCB) will highlight how machine learning approaches can be tailored to making both translational and basic scientific discoveries with biological data. Practitioners at the intersection of computation, machine learning, and biology are in a unique position to frame problems in biomedicine, from drug discovery to vaccination risk scores, and WCB will showcase such recent research. Commodity lab techniques lead to the proliferation of large complex datasets and require new methods to interpret these collections of high-dimensional biological data, such as genetic sequences, cellular features or protein structures and imaging datasets. These data can be used to make new predictions towards clinical response, uncover new biology, or aid in drug discovery.This workshop aims to bring together interdisciplinary machine learning researchers working in areas such as computational genomics; neuroscience; metabolomics; proteomics; bioinformatics; cheminformatics; pathology; radiology; evolutionary biology; population genomics; phenomics; ecology, cancer biology; causality; representation learning and disentanglement to present recent advances and open questions to the machine learning community. We especially encourage interdisciplinary submissions that might not neatly fit into one of these categories.

Whether in robotics, protein design, or physical sciences, one often faces decisions regarding which data to collect or which experiments to perform. There is thus a pressing need for algorithms and sampling strategies that make intelligent decisions about data collection processes that allow for data-efficient learning. Experimental design and active learning have been major research focuses within machine learning and statistics, aiming to answer both theoretical and algorithmic aspects of efficient data collection schemes. The goal of this workshop is to identify missing links that hinder the direct application of these principled research ideas into practically relevant solutions.

A long-standing objective of AI research has been to discover theories of reasoning that are general: accommodating various forms of knowledge and applicable across a diversity of domains. The last two decades have brought steady advances toward this goal, notably in the form of mature theories of probabilistic and causal inference, and in the explosion of reasoning methods built upon the deep learning revolution. However, these advances have only further exposed gaps in both our basic understanding of reasoning and in limitations in the flexibility and composability of automated reasoning technologies. This workshop aims to reinvigorate work on the grand challenge of developing a computational foundation for reasoning in minds, brains, and machines.

In just the past couple of years, we have seen significant advances in the capabilities of (Large) Language Models. One of the most striking capabilities of these systems is knowledge retrieval — Language Models can answer a diverse set of questions, which differ substantially in the domain knowledge needed for their responses, and their input structure. The precise methods for knowledge retrieval vary from the language model directly generating a response (parametric approaches) to a combination of generation and referencing an external knowledge corpus, e.g. retrieval augmented generation, to primarily using an external knowledge corpus with language model embeddings (semi-parametric approaches.) Despite the rapid advances, there remain many pressing open questions on the limits of knowledge retrieval with language models, and connections between these different approaches. How factual are generated responses, and how does this vary with question complexity, model scale, and importantly, different methods of knowledge retrieval? How important is the role of (self-supervised/supervised) pretraining? What are the tradeoffs between few-shot (prompt based) approaches and finetuning when adapting to novel domains? And relatedly, to what extent do different knowledge retrieval approaches generalize to unseen settings? This workshop seeks to bring together a diverse set of researchers across NLP, Machine Learning and Theory to discuss these questions. We hope to share current findings and challenges, identify promising directions for future study, and most importantly, build a community around this topic at this pivotal time.

As modern astrophysical surveys deliver an unprecedented amount of data, from the imaging of hundreds of millions of distant galaxies to the mapping of cosmic radiation fields at ultra-high resolution, conventional data analysis methods are reaching their limits in both computational complexity and optimality. Deep Learning has rapidly been adopted by the astronomical community as a promising way of exploiting these forthcoming big-data datasets and of extracting the physical principles that underlie these complex observations. This has led to an unprecedented exponential growth of publications with in the last year alone about 500 astrophysics papers mentioning deep learning or neural networks in their abstract. Yet, many of these works remain at an exploratory level and have not been translated into real scientific breakthroughs.The goal of this workshop is to bring together Machine Learning researchers and domain experts in the field of Astrophysics to discuss the key open issues which hamper the use of Deep Learning for scientific discovery. Rather than focusing on the benefits of deep learning for astronomy, the proposed workshop aims at overcoming its limitations.Topics that we aim to cover include, but are not limited to, high-dimensional Bayesian inference, simulation-based inference, uncertainty quantification and robustness to covariate shifts, anomaly and outlier detection, symmetries and equivariance. In addition, we plan on hosting meta-research panel discussions on successfully bringing ML to Astrophysics.

Following a series of crippling cyber-attacks that targeted major of the public and social sectors — including schools, hospitals, critical infrastructure, and private businesses — the global community has increased its attention on the wider societal impacts of major cyber security events, forming task forces like the UN Open Ended Working Group on Cyber Security and undertaking policy efforts to mitigate these impacts. These actions are important, but policy changes only represent one side of the solution. On the other are technical developments, within which machine learning has been proposed as a key component of future of cyber defense tools, requiring rapid development to provide the speed and scale needed to detect and respond to new and emerging cyber security threats. Cybersecurity is inherently a systems problem and piece-wise application of off-the-shelf ML tools leave critical gaps in both sophistication and interpretable context needed for comprehensive security systems. To successfully develop ML-based cybersecurity defenses, a greater degree of cross-pollination across the ML and cybersecurity communities is needed because both are highly specialized technical domains. Moreover, the requisite ML topics needed to successfully leverage ML for cybersecurity — such as time series analytics, game theory, deep learning, reinforcement learning, representation learning, semi-supervised and self-supervised learning, learning on large scale streaming data, interpretable and robust autonomous systems, etc. - are foundational to the ICML community.The primary aim of this workshop is to build a mutual comprehensive awareness of the problem and solution spaces across the greater ML community and the Cybersecurity/ML for Cybersecurity communities. To provide meaningful engagement, workshop organizers will curate a program which defines the interdisciplinary boundary and opportunities between machine learning and cybersecurity.

This workshop proposal builds on the success of the 1st Data-Centric AI Workshop organized at NeurIPS 2021 (which attracted more than 160 submissions and close to 200 participants) and expands the effort to engage the deeplearning.ai community with the active interdisciplinary MLCommons community of practitioners, researchers and engineers from both academia and industry by presenting the current state-of-the-art, work-in-progress and a set of open problems in the field of benchmarking data for ML. Many of these areas are in a nascent stage, and we hope to further their development by knitting them together into a coherent whole. We seek to drive progress in addressing these core problems by promoting the creation of a set of benchmarks for data quality and data-related algorithms. We want to bring together work that pushes forward this new view of data-centric ML benchmarks, e.g. the initiatives at MLCommons, a non-profit that operates the MLPerf benchmarks that have become standard for AI chip speed but also others including Dynabench, OpenML, data-centric AI hub, etc. We envision MLCommons as providing a framework and resources for the evolution of benchmarks in this space, and our workshop as showcasing the best innovations revealed by those benchmarks and providing a focus event for the community submitting to them.A huge amount of innovation — in algorithms, ideas, principles, and tools — is needed to make data-centric AI development efficient and effective. We hope that this workshop will help spark that innovation.

Much of the data that is fueling current rapid advances in machine learning is: high dimensional, structurally complex, and strongly nonlinear. This poses challenges for researcher intuition when they ask (i) how and why current algorithms work and (ii) what tools will lead to the next big break-though. Mathematicians working in topology, algebra, and geometry have more than a hundred years worth of finely-developed machinery whose purpose is to give structure to, help build intuition about, and generally better understand spaces and structures beyond those that we can naturally understand. This workshop will show-case work which brings methods from topology, algebra, and geometry and uses them to help answer challenging questions in machine learning. With this workshop we will create a vehicle for disseminating machine learning techniques that utilize rich mathematics and address core challenges described in the ICML call for papers. Additionally, this workshop creates opportunity for presentation of approaches which may address critical, domain-specific ML challenges but do not necessarily demonstrate improved performance on mainstream, data-rich benchmarks. To this end our proposed workshop will open up IMCL to new researchers who in the past were not able to discuss their novel but data set-dependent analysis methods.We interpret topology, algebra, and geometry broadly and welcome submissions ranging from manifold methods to optimal transport to topological data analysis to mathematically informed deep learning. Through intellectual cross-pollination between data-driven and mathematically-inspired communities we believe this workshop will support the continued development of both groups and enable new solutions to problems in machine learning.

Machine learning models often break when deployed in the wild, despite excellent performance on benchmarks. In particular, models can learn to rely on apparently unnatural or irrelevant features. For instance, 1) in detecting lung disease from chest X-rays, models rely on the type of scanner rather than physiological signals, 2) in natural language inference, models rely on the number of shared words rather than the subject’s relationship with the object, 3) in precision medicine, polygenic risk scores for diseases like breast cancer rely on genes prevalent mainly in European populations, and predict poorly in other populations. In examples like these and others, the undesirable behavior stems from the model exploiting a spurious correlation. Improper treatment of spurious correlations can discourage the use of ML in the real world and lead to catastrophic consequences in extreme cases. The recent surge of interest in this issue is accordingly welcome and timely: more than 50 closely related papers have been published just in ICML 2021, NeurIPS 2021, and ICLR 2022. However, the most fundamental questions remain unanswered— e.g., how should the notion of spurious correlations be made precise? How should one evaluate models in the presence of spurious correlations? In which situations can a given method be expected to work, or fail? Which notions of invariance are fruitful and tractable? Further, relevant work has sprung up ad hoc from several distinct communities, with limited interplay between them: invariance and independence-constrained learning in causality-inspired ML, methods to decorrelate predictions and protected features (e.g. race) in algorithmic fairness, and stress testing procedures to discover unexpected model dependencies in reliable ML. This workshop will bring together these different communities to make progress on common foundational problems, and facilitate their interaction with domain-experts to build impactful collaborations.

Formal verification of machine learning-based building blocks is important for complex and critical systems such as autonomous vehicles, medical devices, or cybersecurity systems where guarantees on safety, fault tolerance and correctness are essential. Formal verification of machine learning is an emerging and interdisciplinary field, intersecting with fields of computer-aided verification, programming languages, robotics, computer security, and optimization, with many challenging open problems. This workshop aims to raise awareness of the importance of formal verification methods in the machine learning community and to bring together researchers and practitioners interested in this emerging field from a broad range of disciplines and backgrounds. Organizers of this workshop include pioneering proponents of machine learning verification and six confirmed invited speakers who have solid works in this field with diverse research and demographic backgrounds. The workshop includes posters, contributed talks, and a panel to encourage novel contributed work and interdisciplinary discussions on open challenges.

We propose the 1st ICML Workshop on Safe Learning for Autonomous Driving (SL4AD), as a venue for researchers in artificial intelligence to discuss research problems on autonomous driving, with a specific focus on safe learning. While there have been significant advances in vehicle autonomy (e.g., perception, trajectory forecasting, planning and control, etc.), it is of paramount importance for autonomous systems to adhere to safety specifications, as any safety infraction in urban and highway driving, or high-speed racing, could lead to catastrophic failures. We envision the workshop to bring together regulators, researchers, and industry practitioners from different AI subfields, to work towards safer and more robust autonomous technology. This workshop aims to: (i) highlight open questions about safety issues, when autonomous agents must operate in uncertain and dynamically-complex real-world environments; (ii) bring together researchers and industrial practitioners in autonomous driving with control theoreticians in safety analysis, dependability, and verification; (iii) provide a strong AI benchmark, where the joint evaluation of safety, performance, and generalisation capabilities of AD perception and control algorithms is systematically performed; (iv) provide a forum for discussion among researchers, industrial practitioners, and regulators on the core challenges, promising solution strategies, fundamental limitations, and regulatory realities involved in deploying safety-critical autonomous systems; (v) define new algorithms that handle increasingly complex real-world scenarios---where vehicles must: drive at their physical limits, where any infraction could lead to catastrophic failure, make sub-second decisions in fast-changing environments, and remain robust to distribution shifts, novel road features, and other obstacles, to facilitate cross-domain generalisation.

Adversarial machine learning (AdvML), which aims at tricking ML models by providing deceptive inputs, has been identified as a powerful method to improve various trustworthiness metrics (e.g., adversarial robustness, explainability, and fairness) and to advance versatile ML paradigms (e.g., supervised and self-supervised learning, and static and continual learning). As a consequence of the proliferation of AdvML-inspired research works, the proposed workshop–New Frontiers in AdvML–aims to identify the challenges and limitations of current AdvML methods and explore new prospective and constructive views of AdvML across the full theory/algorithm/application stack. The workshop will explore the new frontiers of AdvML from the following new perspectives: (1) advances in foundational AdvML research, (2) principles and practice of scalable AdvML, and (3) AdvML for good. This will be a full-day workshop, which accepts full paper submissions (up to 6 pages) as well as “blue sky” extended abstract submissions (up to 2 pages).

The 1st Machine Learning for Audio Synthesis workshop at ICML will attempt to cover the space of novel methods and applications of audio generation via machine learning. These include, but are not limited to: methods of speech modeling, environmental sound generation or other forms of ambient sound, novel generative models, music generation in the form of raw audio, and text-to-speech methods. Audio synthesis plays a significant and fundamental role in many audio-based machine learning systems, including smart speakers and voice-based interaction systems, real-time voice modification systems, and music or other content generation systems.We plan to solicit original workshop papers in these areas, some of which will present contributed talks and spotlights. Alongside these presentations will be talks from invited speakers, a poster session and interactive live demo session, and an invited speaker panel.We believe that a machine learning workshop focused around generation in the audio domain would provide a good opportunity to bring together both practitioners of audio generation tools along with core machine learning researchers interested in audio, in order to hopefully forge new directions in this important area of research.

Deep vision models are prone to short-cut learning, vulnerable to adversarial attacks, as well as natural and synthetic image corruptions. While OOD test sets have been proposed to measure the vulnerability of DNNs to distribution shifts of different kinds, it has been shown that the performance on popular OOD test sets such as ImageNet-C or ObjectNet is strongly correlated to the performance on clean ImageNet. Since performance on clean ImageNet clearly tests IID but not OOD generalization, this calls for new challenging OOD datasets testing different aspects of generalization.Our goal is to bring the robustness, domain adaptation, and out-of-distribution detection communities together to work on a new broad-scale benchmark that tests diverse aspects of current computer vision models and guides the way towards the next generation of models. Submissions to this workshop will contain novel datasets, metrics and evaluation settings.

The goal of reinforcement learning (RL) is to maximize a reward signal by taking optimal decisions. An RL system typically contains several moving components, possibly including a policy, a value function, and a model of the environment. We refer to decision awareness as the notion that each of the components and their combination should be explicitly trained to help the agent improve the total amount of collected reward. To better understand decision awareness, consider as an example a model-based method. For environments with rich observations (e.g., pixel-based), the world model is complex and standard approaches would need a large number of samples and a high-capacity function approximator to learn a reasonable approximation of the dynamics. However, a decision-aware agent might recognize that modeling all the granular complexity of the environment is neither feasible nor necessary to learn an optimal policy and instead focus on modeling aspects that are important for decision making. Decision awareness goes beyond the model learning aspect. In actor-critic algorithms, a critic is trained to predict the expected return while later used to aid policy learning. Is return prediction an optimal strategy for critic learning? And, in general, what is the best way to learn each component of an RL system? Our workshop aims at answering these questions and articulating that decision awareness might be a key towards solving grand challenges in RL, including exploration and sample efficiency. The workshop is about decision-aware RL algorithms, their implications, and real-world applications; we focus on decision-aware objectives, end-to-end procedures, and meta-learning techniques for training and discovering components in modular RL systems, as well as theoretical or empirical analyses of the interaction among multiple modules used by RL algorithms.

Recent years have seen a proliferation of models, algorithms, and infrastructure well-suited to complex problems in computational design, from virtual design problems in geometry, program synthesis, and web design to tangible design of molecules, materials, robots, architecture, carpentry, 3D printed models, and other domains. This workshop provides an opportunity for researchers and practitioners to discuss shared problems and solutions in computational design and bridge the gaps between (and within) theory and practice. The workshop will be highly interactive, featuring long talks, short talks, poster sessions, discussion panels, and demos of multiple forms. This is the first workshop of its kind at ICML; we hope that this event will set the stage for many follow-on workshops to come.

Differential privacy is a promising approach to privacy-preserving data analysis. It has been the subject of a decade of intense scientific study, and has now been deployed in products at government agencies such as the U.S. Census Bureau and companies like Microsoft, Apple, and Google. MIT Technology Review named differential privacy one of 10 breakthrough technologies of 2020.Since data privacy is a pervasive concern, differential privacy has been studied by researchers from many distinct communities, including machine learning, statistics, algorithms, computer security, cryptography, databases, data mining, programming languages, social sciences, and law. We believe that this combined effort across a broad spectrum of computer science is essential for differential privacy to realize its full potential. To this end, our workshop will stimulate discussion among participants about both the state-of-the-art in differential privacy and the future challenges that must be addressed to make differential privacy more practical.

Deep networks have shown outstanding scaling properties both in terms of data and model sizes: larger does better. Unfortunately, the computational cost of current state-of-the-art methods is prohibitive. A number of new techniques have recently arisen to address and improve this fundamental quality-cost trade-off. For instance, methods like conditional computation, adaptive computation, dynamic model sparsification, and early-exit approaches are all aimed at addressing the above-mentioned quality-cost trade-off. This workshop explores such exciting and practically-relevant research avenues.More specifically, as part of contributed content we will invite high-quality papers on the following topics: dynamic routing, mixture-of-experts models, early-exit methods, conditional computations, capsules and object-oriented learning, reusable components, online network growing and pruning, online neural architecture search and applications of dynamic networks (continual learning, wireless/embedded devices and similar).The workshop is planned as a whole day event and will feature 2 keynote talks, a mix of panel discussion, contributed and invited talks, and a poster session. The invited speakers cover a diverse range of research fields (machine learning, computer vision, neuroscience, natural language processing) and backgrounds (academic, industry) and include speakers from underrepresented groups. All speakers confirmed their talks and the list ranges from senior faculty members (Gao Huang, Tinne Tuytelaars) to applied and theoretical research scientists (Weinan Sun, Francesco Locatello). The workshop builds on a set of previous workshops previously run at prime venues, such as CVPR, NeurIPS and ICLR.

Do you have a crazy idea about Machine Learning? Have you figured out AGI? Or an algorithm to generate personalized memes? Pitch your crazy idea in this ICML social. We invite attendees to pitch their fun, crazy or wild ideas relevant to the ICML audience. Every speaker would give a 2-minute presentation/pitch to the audience.

"Webinar and Q&A on How to Negotiate Industry Offers in AI. Some of the topics we discuss are: * What the standard recruiting process looks like * How to choose the best job offer for career growth * When/how you should negotiate * When should you walk away from a job offer * When can an offer be rescinded from negotiating"